Spencer Kinsey

The paper develops a timescale-aligned framework to measure amplitude balance in brain networks and finds greater imbalance and slower recovery in schizophrenia, linked to symptoms and cognition.

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

This paper introduces a structural co-modulation measure to quantify individualized interactions between brain components using source-based morphometry, finding reduced co-modulation in schizophrenia patients.

This study uses explicitly nonlinear fMRI connectivity to uncover developmental trajectories of brain networks in infants, revealing insights into key functional systems.

This study introduces normalized dynamic time warping to improve sensitivity in detecting functional network connectivity differences in schizophrenia compared to standard DTW and correlation methods.

This paper introduces a nonlinear method to estimate brain networks from fMRI data, showing it better distinguishes schizophrenia patients from controls than linear methods.